{"product_id":"simulation-based-optimization-parametric-optimization-techniques-and-reinforcement-learning-9781489977311","title":"Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning","description":"\u003cp\u003e\u003cb\u003e\u003ci\u003eSimulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning\u003c\/i\u003e\u003c\/b\u003e introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are \u003ci\u003emodel-free\u003c\/i\u003e optimization techniques - especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey features of this revised and improved Second Edition include: \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e- Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms)\u003c\/p\u003e\u003cp\u003e- Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming(value and policy iteration) for discounted, average, and total reward performance metrics\u003c\/p\u003e\u003cp\u003e- An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: \u003ci\u003eQ\u003c\/i\u003e-\u003ci\u003eLearning\u003c\/i\u003e, \u003ci\u003eSARSA\u003c\/i\u003e, and \u003ci\u003eR-SMART \u003c\/i\u003ealgorithms, and policy search, via \u003ci\u003eAPI\u003c\/i\u003e, \u003ci\u003eQ\u003c\/i\u003e-\u003ci\u003eP\u003c\/i\u003e-\u003ci\u003eLearning\u003c\/i\u003e, actor-critics, and learning automata\u003c\/p\u003e\u003cp\u003e- A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations\u003c\/p\u003e\u003cp\u003eThemed around three areas in separate sets of chapters - \u003cb\u003eStatic Simulation Optimization, Reinforcement Learning \u003c\/b\u003eand\u003cb\u003e Convergence Analysis\u003c\/b\u003e\u003ci\u003e \u003c\/i\u003e- this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Abhijit Gosavi\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Springer\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/10\/2016\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 508\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.64lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.21h x 6.14w x 1.08d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781489977311\u003cbr\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbhijit Gosav\u003c\/b\u003ei is a leading international authority on reinforcement learning, stochastic dynamic programming and simulation-based optimization. The first edition of his Springer book \"Simulation-Based Optimization\" that appeared in 2003 was the first text to have appeared on that topic. He is regularly an invited speaker at major national and international conferences on operations research, reinforcement learning, adaptive\/approximate dynamic programming, and systems engineering.\u003c\/p\u003e\u003cp\u003eHe has published more than fifty journal and conference articles - many of which have appeared in leading scholarly journals such as \u003ci\u003eManagement Science\u003c\/i\u003e, \u003ci\u003eAutomatica\u003c\/i\u003e, \u003ci\u003eINFORMS Journal on Computing\u003c\/i\u003e, \u003ci\u003eMachine Learning\u003c\/i\u003e, \u003ci\u003eJournal of Retailing\u003c\/i\u003e, \u003ci\u003eSystems and Control Letters\u003c\/i\u003e and the \u003ci\u003eEuropean Journal of Operational Research\u003c\/i\u003e. He has also authored numerous book chapters on simulation-based optimization and operations research. His research has been funded by the National Science Foundation, Department of Defense, Missouri Department of Transportation, University of Missouri Research Board and industry. He has consulted extensively for the U.S. Department of Veterans Affairs and the mass media as a statistical\/simulation analyst. He has received teaching awards from the Institute of Industrial Engineers.\u003c\/p\u003e\u003cp\u003eHe currently serves as an Associate Professor of Engineering Management and Systems Engineering at Missouri University of Science and Technology in Rolla, MO. He holds a masters degree in Mechanical Engineering from the Indian Institute of Technology and a Ph.D. in Industrial Engineering from the University of South Florida. He is a member of INFORMS, IIE and ASEE.\u003c\/p\u003e\u003cbr\u003e","brand":"Springer","offers":[{"title":"Paperback","offer_id":45352075788403,"sku":"9781489977311","price":207.95,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0555\/9255\/0515\/files\/img_f7799ce0-b52c-4bec-a3cf-8651cfb2c63c.jpg?v=1786548919","url":"https:\/\/bookstorenmore.com\/en-de\/products\/simulation-based-optimization-parametric-optimization-techniques-and-reinforcement-learning-9781489977311","provider":"Bookstore N More","version":"1.0","type":"link"}